Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations973
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.1 KiB
Average record size in memory120.1 B

Variable types

Numeric11
Categorical4

Alerts

BMI is highly overall correlated with Weight (kg)High correlation
Calories_Burned is highly overall correlated with Experience_Level and 2 other fieldsHigh correlation
Experience_Level is highly overall correlated with Calories_Burned and 4 other fieldsHigh correlation
Fat_Percentage is highly overall correlated with Calories_Burned and 3 other fieldsHigh correlation
Gender is highly overall correlated with Fat_Percentage and 3 other fieldsHigh correlation
Height (m) is highly overall correlated with GenderHigh correlation
Session_Duration (hours) is highly overall correlated with Calories_Burned and 1 other fieldsHigh correlation
Water_Intake (liters) is highly overall correlated with Experience_Level and 2 other fieldsHigh correlation
Weight (kg) is highly overall correlated with BMI and 1 other fieldsHigh correlation
Workout_Frequency (days/week) is highly overall correlated with Experience_LevelHigh correlation

Reproduction

Analysis started2024-11-28 19:02:43.246865
Analysis finished2024-11-28 19:02:59.669799
Duration16.42 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct42
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.683453
Minimum18
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:02:59.859465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q128
median40
Q349
95-th percentile57
Maximum59
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.180928
Coefficient of variation (CV)0.31488729
Kurtosis-1.2150761
Mean38.683453
Median Absolute Deviation (MAD)10
Skewness-0.077863962
Sum37639
Variance148.375
MonotonicityNot monotonic
2024-11-28T20:03:00.010468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
43 34
 
3.5%
50 33
 
3.4%
52 32
 
3.3%
45 30
 
3.1%
54 30
 
3.1%
18 27
 
2.8%
42 27
 
2.8%
22 27
 
2.8%
56 27
 
2.8%
25 26
 
2.7%
Other values (32) 680
69.9%
ValueCountFrequency (%)
18 27
2.8%
19 26
2.7%
20 25
2.6%
21 20
2.1%
22 27
2.8%
23 25
2.6%
24 15
1.5%
25 26
2.7%
26 21
2.2%
27 18
1.8%
ValueCountFrequency (%)
59 19
2.0%
58 19
2.0%
57 23
2.4%
56 27
2.8%
55 18
1.8%
54 30
3.1%
53 23
2.4%
52 32
3.3%
51 19
2.0%
50 33
3.4%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Male
511 
Female
462 

Length

Max length6
Median length4
Mean length4.9496403
Min length4

Characters and Unicode

Total characters4816
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 511
52.5%
Female 462
47.5%

Length

2024-11-28T20:03:00.237467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T20:03:00.370493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 511
52.5%
female 462
47.5%

Most occurring characters

ValueCountFrequency (%)
e 1435
29.8%
a 973
20.2%
l 973
20.2%
M 511
 
10.6%
F 462
 
9.6%
m 462
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1435
29.8%
a 973
20.2%
l 973
20.2%
M 511
 
10.6%
F 462
 
9.6%
m 462
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1435
29.8%
a 973
20.2%
l 973
20.2%
M 511
 
10.6%
F 462
 
9.6%
m 462
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1435
29.8%
a 973
20.2%
l 973
20.2%
M 511
 
10.6%
F 462
 
9.6%
m 462
 
9.6%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct532
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.854676
Minimum40
Maximum129.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:00.492493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile45.46
Q158.1
median70
Q386
95-th percentile118.16
Maximum129.9
Range89.9
Interquartile range (IQR)27.9

Descriptive statistics

Standard deviation21.2075
Coefficient of variation (CV)0.28715176
Kurtosis-0.023968988
Mean73.854676
Median Absolute Deviation (MAD)13.4
Skewness0.772384
Sum71860.6
Variance449.75808
MonotonicityNot monotonic
2024-11-28T20:03:00.637465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.7 6
 
0.6%
75.6 6
 
0.6%
81.4 5
 
0.5%
64.5 5
 
0.5%
102.6 5
 
0.5%
50.3 5
 
0.5%
71.2 5
 
0.5%
64.3 5
 
0.5%
59 5
 
0.5%
65.2 5
 
0.5%
Other values (522) 921
94.7%
ValueCountFrequency (%)
40 2
0.2%
40.3 1
0.1%
40.4 2
0.2%
40.5 2
0.2%
40.6 1
0.1%
40.9 1
0.1%
41.1 1
0.1%
41.2 2
0.2%
41.6 1
0.1%
41.9 1
0.1%
ValueCountFrequency (%)
129.9 1
 
0.1%
129.5 1
 
0.1%
129.2 1
 
0.1%
129 1
 
0.1%
128.4 3
0.3%
128.2 1
 
0.1%
127.9 1
 
0.1%
127.8 1
 
0.1%
127.7 1
 
0.1%
127.6 1
 
0.1%

Height (m)
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7225797
Minimum1.5
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:00.787498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.53
Q11.62
median1.71
Q31.8
95-th percentile1.954
Maximum2
Range0.5
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.12771989
Coefficient of variation (CV)0.074144551
Kurtosis-0.72363267
Mean1.7225797
Median Absolute Deviation (MAD)0.09
Skewness0.33885837
Sum1676.07
Variance0.016312371
MonotonicityNot monotonic
2024-11-28T20:03:00.944498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.62 44
 
4.5%
1.76 37
 
3.8%
1.77 37
 
3.8%
1.61 36
 
3.7%
1.79 32
 
3.3%
1.68 31
 
3.2%
1.71 30
 
3.1%
1.63 29
 
3.0%
1.72 28
 
2.9%
1.6 27
 
2.8%
Other values (41) 642
66.0%
ValueCountFrequency (%)
1.5 11
1.1%
1.51 11
1.1%
1.52 22
2.3%
1.53 14
1.4%
1.54 12
1.2%
1.55 20
2.1%
1.56 14
1.4%
1.57 18
1.8%
1.58 20
2.1%
1.59 15
1.5%
ValueCountFrequency (%)
2 9
0.9%
1.99 15
1.5%
1.98 9
0.9%
1.97 7
0.7%
1.96 9
0.9%
1.95 14
1.4%
1.94 12
1.2%
1.93 14
1.4%
1.92 15
1.5%
1.91 10
1.0%

Max_BPM
Real number (ℝ)

Distinct40
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.88386
Minimum160
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:01.090497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile162
Q1170
median180
Q3190
95-th percentile198
Maximum199
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.525686
Coefficient of variation (CV)0.064072928
Kurtosis-1.1879155
Mean179.88386
Median Absolute Deviation (MAD)10
Skewness-0.037950486
Sum175027
Variance132.84144
MonotonicityNot monotonic
2024-11-28T20:03:01.269464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
198 34
 
3.5%
188 31
 
3.2%
177 31
 
3.2%
161 30
 
3.1%
185 29
 
3.0%
192 28
 
2.9%
182 28
 
2.9%
184 28
 
2.9%
164 28
 
2.9%
187 28
 
2.9%
Other values (30) 678
69.7%
ValueCountFrequency (%)
160 18
1.8%
161 30
3.1%
162 25
2.6%
163 17
1.7%
164 28
2.9%
165 21
2.2%
166 22
2.3%
167 21
2.2%
168 27
2.8%
169 20
2.1%
ValueCountFrequency (%)
199 26
2.7%
198 34
3.5%
197 17
1.7%
196 26
2.7%
195 23
2.4%
194 27
2.8%
193 22
2.3%
192 28
2.9%
191 22
2.3%
190 21
2.2%

Avg_BPM
Real number (ℝ)

Distinct50
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.7667
Minimum120
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:01.457465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile122
Q1131
median143
Q3156
95-th percentile167
Maximum169
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.345101
Coefficient of variation (CV)0.099780417
Kurtosis-1.1987236
Mean143.7667
Median Absolute Deviation (MAD)12
Skewness0.086360959
Sum139885
Variance205.78194
MonotonicityNot monotonic
2024-11-28T20:03:01.633465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 27
 
2.8%
130 26
 
2.7%
129 25
 
2.6%
139 25
 
2.6%
136 24
 
2.5%
135 24
 
2.5%
161 24
 
2.5%
158 24
 
2.5%
120 23
 
2.4%
131 23
 
2.4%
Other values (40) 728
74.8%
ValueCountFrequency (%)
120 23
2.4%
121 23
2.4%
122 16
1.6%
123 17
1.7%
124 12
1.2%
125 21
2.2%
126 18
1.8%
127 21
2.2%
128 22
2.3%
129 25
2.6%
ValueCountFrequency (%)
169 13
1.3%
168 22
2.3%
167 22
2.3%
166 19
2.0%
165 19
2.0%
164 10
1.0%
163 14
1.4%
162 19
2.0%
161 24
2.5%
160 21
2.2%

Resting_BPM
Real number (ℝ)

Distinct25
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.223022
Minimum50
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:01.797464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q156
median62
Q368
95-th percentile73
Maximum74
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3270599
Coefficient of variation (CV)0.11775481
Kurtosis-1.1814657
Mean62.223022
Median Absolute Deviation (MAD)6
Skewness-0.071635902
Sum60543
Variance53.685807
MonotonicityNot monotonic
2024-11-28T20:03:01.935466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
50 59
 
6.1%
58 48
 
4.9%
66 48
 
4.9%
73 46
 
4.7%
74 45
 
4.6%
67 44
 
4.5%
65 43
 
4.4%
62 41
 
4.2%
68 40
 
4.1%
64 39
 
4.0%
Other values (15) 520
53.4%
ValueCountFrequency (%)
50 59
6.1%
51 25
2.6%
52 37
3.8%
53 39
4.0%
54 37
3.8%
55 33
3.4%
56 34
3.5%
57 24
2.5%
58 48
4.9%
59 37
3.8%
ValueCountFrequency (%)
74 45
4.6%
73 46
4.7%
72 39
4.0%
71 37
3.8%
70 33
3.4%
69 37
3.8%
68 40
4.1%
67 44
4.5%
66 48
4.9%
65 43
4.4%

Session_Duration (hours)
Real number (ℝ)

High correlation 

Distinct147
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2564234
Minimum0.5
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:02.086464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.66
Q11.04
median1.26
Q31.46
95-th percentile1.87
Maximum2
Range1.5
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.34303348
Coefficient of variation (CV)0.27302378
Kurtosis-0.35080519
Mean1.2564234
Median Absolute Deviation (MAD)0.2
Skewness0.025761027
Sum1222.5
Variance0.11767197
MonotonicityNot monotonic
2024-11-28T20:03:02.266498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13 20
 
2.1%
1.37 20
 
2.1%
1.03 20
 
2.1%
1.08 19
 
2.0%
1.42 18
 
1.8%
1.38 18
 
1.8%
1.29 17
 
1.7%
1.36 17
 
1.7%
1.14 16
 
1.6%
1.41 15
 
1.5%
Other values (137) 793
81.5%
ValueCountFrequency (%)
0.5 1
 
0.1%
0.51 5
0.5%
0.52 3
0.3%
0.53 2
 
0.2%
0.54 5
0.5%
0.55 4
0.4%
0.56 3
0.3%
0.57 3
0.3%
0.58 3
0.3%
0.59 2
 
0.2%
ValueCountFrequency (%)
2 1
 
0.1%
1.99 5
0.5%
1.98 6
0.6%
1.97 6
0.6%
1.96 2
 
0.2%
1.95 2
 
0.2%
1.94 3
0.3%
1.93 6
0.6%
1.91 3
0.3%
1.9 5
0.5%

Calories_Burned
Real number (ℝ)

High correlation 

Distinct621
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean905.4224
Minimum303
Maximum1783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:02.423466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum303
5-th percentile448
Q1720
median893
Q31076
95-th percentile1378
Maximum1783
Range1480
Interquartile range (IQR)356

Descriptive statistics

Standard deviation272.64152
Coefficient of variation (CV)0.3011208
Kurtosis-0.056049954
Mean905.4224
Median Absolute Deviation (MAD)178
Skewness0.2783211
Sum880976
Variance74333.396
MonotonicityNot monotonic
2024-11-28T20:03:02.609471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
883 6
 
0.6%
1025 6
 
0.6%
832 5
 
0.5%
875 5
 
0.5%
711 4
 
0.4%
886 4
 
0.4%
927 4
 
0.4%
796 4
 
0.4%
742 4
 
0.4%
937 4
 
0.4%
Other values (611) 927
95.3%
ValueCountFrequency (%)
303 1
0.1%
319 1
0.1%
330 1
0.1%
331 1
0.1%
333 1
0.1%
350 1
0.1%
353 1
0.1%
354 2
0.2%
362 1
0.1%
363 1
0.1%
ValueCountFrequency (%)
1783 1
0.1%
1766 1
0.1%
1725 1
0.1%
1701 1
0.1%
1688 1
0.1%
1675 1
0.1%
1646 1
0.1%
1634 1
0.1%
1625 1
0.1%
1622 1
0.1%

Workout_Type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Strength
258 
Cardio
255 
Yoga
239 
HIIT
221 

Length

Max length8
Median length6
Mean length5.5847893
Min length4

Characters and Unicode

Total characters5434
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoga
2nd rowHIIT
3rd rowCardio
4th rowStrength
5th rowStrength

Common Values

ValueCountFrequency (%)
Strength 258
26.5%
Cardio 255
26.2%
Yoga 239
24.6%
HIIT 221
22.7%

Length

2024-11-28T20:03:02.773498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T20:03:02.903467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
strength 258
26.5%
cardio 255
26.2%
yoga 239
24.6%
hiit 221
22.7%

Most occurring characters

ValueCountFrequency (%)
t 516
 
9.5%
r 513
 
9.4%
g 497
 
9.1%
a 494
 
9.1%
o 494
 
9.1%
I 442
 
8.1%
e 258
 
4.7%
S 258
 
4.7%
n 258
 
4.7%
h 258
 
4.7%
Other values (6) 1446
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 516
 
9.5%
r 513
 
9.4%
g 497
 
9.1%
a 494
 
9.1%
o 494
 
9.1%
I 442
 
8.1%
e 258
 
4.7%
S 258
 
4.7%
n 258
 
4.7%
h 258
 
4.7%
Other values (6) 1446
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 516
 
9.5%
r 513
 
9.4%
g 497
 
9.1%
a 494
 
9.1%
o 494
 
9.1%
I 442
 
8.1%
e 258
 
4.7%
S 258
 
4.7%
n 258
 
4.7%
h 258
 
4.7%
Other values (6) 1446
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 516
 
9.5%
r 513
 
9.4%
g 497
 
9.1%
a 494
 
9.1%
o 494
 
9.1%
I 442
 
8.1%
e 258
 
4.7%
S 258
 
4.7%
n 258
 
4.7%
h 258
 
4.7%
Other values (6) 1446
26.6%

Fat_Percentage
Real number (ℝ)

High correlation 

Distinct239
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.976773
Minimum10
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:03.068492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12.32
Q121.3
median26.2
Q329.3
95-th percentile33.74
Maximum35
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.2594188
Coefficient of variation (CV)0.25060959
Kurtosis-0.33901946
Mean24.976773
Median Absolute Deviation (MAD)3.7
Skewness-0.63522468
Sum24302.4
Variance39.180324
MonotonicityNot monotonic
2024-11-28T20:03:03.238492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.1 14
 
1.4%
27.3 13
 
1.3%
21.3 11
 
1.1%
29.8 11
 
1.1%
25.7 11
 
1.1%
27.6 11
 
1.1%
28.2 11
 
1.1%
28.4 11
 
1.1%
25.3 11
 
1.1%
29 11
 
1.1%
Other values (229) 858
88.2%
ValueCountFrequency (%)
10 1
 
0.1%
10.1 3
0.3%
10.2 3
0.3%
10.3 2
0.2%
10.4 1
 
0.1%
10.5 3
0.3%
10.6 2
0.2%
10.7 4
0.4%
10.9 1
 
0.1%
11 3
0.3%
ValueCountFrequency (%)
35 2
 
0.2%
34.9 6
0.6%
34.8 4
0.4%
34.7 5
0.5%
34.6 3
0.3%
34.5 3
0.3%
34.4 3
0.3%
34.3 6
0.6%
34.2 2
 
0.2%
34.1 5
0.5%

Water_Intake (liters)
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6266187
Minimum1.5
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:03.542475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.7
Q12.2
median2.6
Q33.1
95-th percentile3.5
Maximum3.7
Range2.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.60017194
Coefficient of variation (CV)0.22849603
Kurtosis-1.0202979
Mean2.6266187
Median Absolute Deviation (MAD)0.5
Skewness0.071479902
Sum2555.7
Variance0.36020635
MonotonicityNot monotonic
2024-11-28T20:03:03.693465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3.5 124
 
12.7%
2.7 123
 
12.6%
2.3 59
 
6.1%
2.1 57
 
5.9%
2.4 55
 
5.7%
2.6 50
 
5.1%
2.2 49
 
5.0%
2 46
 
4.7%
2.5 45
 
4.6%
3.4 39
 
4.0%
Other values (13) 326
33.5%
ValueCountFrequency (%)
1.5 20
 
2.1%
1.6 26
2.7%
1.7 27
2.8%
1.8 34
3.5%
1.9 31
3.2%
2 46
4.7%
2.1 57
5.9%
2.2 49
5.0%
2.3 59
6.1%
2.4 55
5.7%
ValueCountFrequency (%)
3.7 14
 
1.4%
3.6 18
 
1.8%
3.5 124
12.7%
3.4 39
 
4.0%
3.3 22
 
2.3%
3.2 22
 
2.3%
3.1 29
 
3.0%
3 22
 
2.3%
2.9 34
 
3.5%
2.8 27
 
2.8%

Workout_Frequency (days/week)
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
3
368 
4
306 
2
197 
5
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters973
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Length

2024-11-28T20:03:03.819495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T20:03:03.932493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Most occurring characters

ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 368
37.8%
4 306
31.4%
2 197
20.2%
5 102
 
10.5%

Experience_Level
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2
406 
1
376 
3
191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters973
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

Length

2024-11-28T20:03:04.052465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T20:03:04.165493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

Most occurring characters

ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 406
41.7%
1 376
38.6%
3 191
19.6%

BMI
Real number (ℝ)

High correlation 

Distinct771
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.912127
Minimum12.32
Maximum49.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-11-28T20:03:04.291475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.32
5-th percentile15.448
Q120.11
median24.16
Q328.56
95-th percentile37.542
Maximum49.84
Range37.52
Interquartile range (IQR)8.45

Descriptive statistics

Standard deviation6.6608794
Coefficient of variation (CV)0.26737497
Kurtosis0.74324036
Mean24.912127
Median Absolute Deviation (MAD)4.27
Skewness0.76364786
Sum24239.5
Variance44.367314
MonotonicityNot monotonic
2024-11-28T20:03:04.466465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.53 5
 
0.5%
22.48 4
 
0.4%
23.79 4
 
0.4%
24.31 4
 
0.4%
26.47 4
 
0.4%
23.88 4
 
0.4%
21.16 4
 
0.4%
21.87 3
 
0.3%
22.44 3
 
0.3%
34.06 3
 
0.3%
Other values (761) 935
96.1%
ValueCountFrequency (%)
12.32 1
0.1%
12.47 1
0.1%
12.67 1
0.1%
12.73 1
0.1%
12.85 1
0.1%
12.91 1
0.1%
12.97 1
0.1%
13.03 1
0.1%
13.23 1
0.1%
13.36 1
0.1%
ValueCountFrequency (%)
49.84 1
0.1%
48.43 1
0.1%
47.72 1
0.1%
46.98 1
0.1%
46.94 1
0.1%
46.9 1
0.1%
45.49 1
0.1%
45.43 1
0.1%
45.14 2
0.2%
44.84 1
0.1%

Interactions

2024-11-28T20:02:57.602510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:43.862237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.065171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.341853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.800857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.312085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.691087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.975467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.451467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.725556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.082511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.759508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:43.979171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.164203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.458852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.926879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.411086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.822086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.091466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.559467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.823556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.193511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.881510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.078172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.339852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.563884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.056854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.523088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.935088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.222467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.668587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.924555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.308511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.993510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.184171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.442884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.677852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.175884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.640121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.043118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.348467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.777556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.050559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.415509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.114510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.300203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.561885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.815853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.299856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.762150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.158501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.578467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.902556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.169555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.580513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.231509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.407198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.664884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.974879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.516852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.924086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.263503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.694467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.010589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.295367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.704510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.403512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.511171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.770880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.124856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.637087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.056085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.372469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.815472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.122555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.404395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.829515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.641512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.627203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:45.884852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.288856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.787088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.195089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.507466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:52.942499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.241555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.520512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:56.979514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.806525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.740170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.009856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.428853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:48.932088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.343086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.626466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.066466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.357589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.699511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.130510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:58.932513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.852175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.120879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.553853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.060086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.468103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.737467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.180468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.477556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.817510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.245510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:59.055511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:44.954171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:46.227885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:47.674852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:49.186085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:50.576086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:51.842482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:53.298467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:54.606556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:55.931513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-28T20:02:57.480542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-28T20:03:04.607468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAvg_BPMBMICalories_BurnedExperience_LevelFat_PercentageGenderHeight (m)Max_BPMResting_BPMSession_Duration (hours)Water_Intake (liters)Weight (kg)Workout_Frequency (days/week)Workout_Type
Age1.0000.036-0.018-0.1530.000-0.0090.068-0.019-0.0160.005-0.0190.043-0.0320.0000.000
Avg_BPM0.0361.000-0.0030.3370.0230.0020.000-0.008-0.0380.0600.020-0.0060.0030.0360.014
BMI-0.018-0.0031.0000.0730.166-0.1350.374-0.1700.076-0.0380.0210.2060.8350.0980.000
Calories_Burned-0.1530.3370.0731.0000.606-0.5060.1490.075-0.008-0.0030.9030.3460.1320.3880.056
Experience_Level0.0000.0230.1660.6061.0000.6950.0000.0480.0000.0000.7710.5150.3510.7210.000
Fat_Percentage-0.0090.002-0.135-0.5060.6951.0000.761-0.250-0.011-0.005-0.481-0.581-0.2770.4320.000
Gender0.0680.0000.3740.1490.0000.7611.0000.6250.0000.0150.0000.7120.6370.0000.000
Height (m)-0.019-0.008-0.1700.0750.048-0.2500.6251.000-0.017-0.001-0.0060.3840.3640.0430.000
Max_BPM-0.016-0.0380.076-0.0080.000-0.0110.000-0.0171.0000.0370.0030.0300.0550.0460.000
Resting_BPM0.0050.060-0.038-0.0030.000-0.0050.015-0.0010.0371.000-0.0220.008-0.0350.0000.000
Session_Duration (hours)-0.0190.0200.0210.9030.771-0.4810.000-0.0060.003-0.0221.0000.2850.0370.4750.032
Water_Intake (liters)0.043-0.0060.2060.3460.515-0.5810.7120.3840.0300.0080.2851.0000.4110.3130.000
Weight (kg)-0.0320.0030.8350.1320.351-0.2770.6370.3640.055-0.0350.0370.4111.0000.2200.000
Workout_Frequency (days/week)0.0000.0360.0980.3880.7210.4320.0000.0430.0460.0000.4750.3130.2201.0000.000
Workout_Type0.0000.0140.0000.0560.0000.0000.0000.0000.0000.0000.0320.0000.0000.0001.000

Missing values

2024-11-28T20:02:59.235537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-28T20:02:59.484106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderWeight (kg)Height (m)Max_BPMAvg_BPMResting_BPMSession_Duration (hours)Calories_BurnedWorkout_TypeFat_PercentageWater_Intake (liters)Workout_Frequency (days/week)Experience_LevelBMI
056Male88.31.71180157601.691313.0Yoga12.63.54330.20
146Female74.91.53179151661.30883.0HIIT33.92.14232.00
232Female68.11.66167122541.11677.0Cardio33.42.34224.71
325Male53.21.70190164560.59532.0Strength28.82.13118.41
438Male46.11.79188158680.64556.0Strength29.22.83114.39
556Female58.01.68168156741.591116.0HIIT15.52.75320.55
636Male70.31.72174169731.491385.0Cardio21.32.33223.76
740Female69.71.51189141641.27895.0Cardio30.61.93230.57
828Male121.71.94185127521.03719.0Strength28.92.64232.34
928Male101.81.84169136641.08808.0Cardio29.72.73130.07
AgeGenderWeight (kg)Height (m)Max_BPMAvg_BPMResting_BPMSession_Duration (hours)Calories_BurnedWorkout_TypeFat_PercentageWater_Intake (liters)Workout_Frequency (days/week)Experience_LevelBMI
96357Female43.81.75180160731.391001.0Cardio25.11.72114.30
96456Female64.21.69190137611.991227.0Cardio19.62.75322.48
96523Female44.11.62196122580.58354.0Yoga25.72.72116.80
96623Male87.31.91164129581.871327.0HIIT11.83.55323.93
96720Male55.01.60172168671.121035.0Yoga24.03.24221.48
96824Male87.11.74187158671.571364.0Strength10.03.54328.77
96925Male66.61.61184166561.381260.0Strength25.03.02125.69
97059Female60.41.76194120531.72929.0Cardio18.82.75319.50
97132Male126.41.83198146621.10883.0HIIT28.22.13237.74
97246Male88.71.63166146660.75542.0Strength28.83.52133.38